• Deep Learning Specialization 筆記
  • Introduction
  • Lecture 1 - Neural Networks and Deep Learning
    • Week1 Introduction to Deep Learning
    • Week2 LogisticRegression as a NeuralNetwork
    • Week2 Python and Vectorization
    • Week3 Shallow Neural Network
    • Week4 Deep Neural Network
  • Lecture 2 - Improving Deep Neural Networks
    • Week1 DataSets, Regularize, Optimization
  • Lecture 3 - Structuring Machine Learning Projects
    • Week1: ML Strategy(1)
  • Lecture 4 - Convolutional Neural Networks
    • W1: Foundations of CNN
    • W2: Case studies
    • W2: Practical advices for using Convnets
    • W3: Detection Algorithms
    • W4: Special Applications - Face recognition & Neural style transfer
      • Face Recognition
      • Neural Style Transfer
      • 1D and 3D Generalizations
  • Lecture 5 - Sequence Models
    • Week1: Recurrent Neural Networks
    • Week2: Word Embedding
    • Week3: Seq2seq models
    • 一般NN with sliding window 和 RNN 的區別在哪
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Introduction

Coursera lecture note

專項課程: Deep Learning Specialization

子課程 1: Neural Networks and Deep Learning

子課程 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

子課程 3: Structuring Machine Learning Projects

子課程 4: Convolutional Neural Networks

子課程 5: Sequence Models

全課程 中文筆記影片

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